三维重建中的大规模航空影像检索方法
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摘要
针对互联网获取的航空影像数据或非摄影测量专业人员拍摄的无人机影像数据进行高精度测绘与三维重建时,从中检索相似影像较为困难的问题,该文提出了一种基于尺度不变特征变换(SIFT)算子与词汇树的大规模航空影像检索方法。该方法可以从规模庞大、排序复杂的航空影像中快速准确地检索出相似影像。首先利用SIFT描述子来代表影像,然后通过分层K-means算法对影像库的SIFT描述子构建词汇树,最后再利用TF-IDF方法加权计算待查询影像与影像集中各影像的相似度,获得相似影像。通过对多组大规模航空影像数据进行试验,结果证明了该文所提算法的可靠性、准确性和实用性。
Aiming at the problem of how to retrieve images with overlapping regions quickly and accurately while using these image data obtained from the Internet or non-photogrammetry professionals in unmanned aerial vehicle(UAV)imaging data for high-precision mapping and 3 Dreconstruction,this paper proposed a large-scale aerial image retrieval method based on the scale invariant feature transform(SIFT)operator and the vocabulary tree.This method could quickly and accurately retrieve similar images from massive and unordered aerial images.Firstly,the SIFT descriptor was used to represent the image and then the vocabulary tree was built using SIFT descriptors in the image library through the hierarchical K-means algorithm.Finally,the term frequency-inverse document frequency method which calculated similarity in query images and images collection were further utilized to obtain the similar images.Experiments with large-scale aerial images showed the reliability,accuracy and actual performance of the proposed algorithm.
Aiming at the problem of how to retrieve images with overlapping regions quickly and accurately while using these image data obtained from the Internet or non-photogrammetry professionals in unmanned aerial vehicle(UAV)imaging data for high-precision mapping and 3 Dreconstruction,this paper proposed a large-scale aerial image retrieval method based on the scale invariant feature transform(SIFT)operator and the vocabulary tree.This method could quickly and accurately retrieve similar images from massive and unordered aerial images.Firstly,the SIFT descriptor was used to represent the image and then the vocabulary tree was built using SIFT descriptors in the image library through the hierarchical K-means algorithm.Finally,the term frequency-inverse document frequency method which calculated similarity in query images and images collection were further utilized to obtain the similar images.Experiments with large-scale aerial images showed the reliability,accuracy and actual performance of the proposed algorithm.
引文
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[19]何海清.低空摄影测量航带重构及数据处理方法研究[J].测绘学报,2014,43(4):440.(HE Haiqing.The study of strips reconstruction oriented data processing methodology in low-altitude photogrammetry[J].Acta Geodaetica et Cartographica Sinica,2014,43(4):440.)
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[21]张剑清,潘励,王树根.摄影测量学[M].2版.武汉:武汉大学出版社,2009.(ZHANG Jianqing,PAN Li,WANG Shugen.Photogrammetry[M].2nd ed.Wuhan:Wuhan University Press,2009.)
[2] HALAWANI A,TEYNOR A,SETIA L,et al.Fundamentals and applications of image retrieval:an overview[J].Datenbank-Spektrum,2006(18):14-23.
[3] BAUMBERG A.Reliable feature matching across widely separated views[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington,DC,USA:IEEE Computer Society,2000:774-781.
[4] BEIS J S,LOWE D G.Shape indexing using approximate nearest-neighbour search in high-dimensional spaces[C]∥Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington,DC,USA:IEEE Computer Society,1997:1000-1006.
[5] MIKOLAJCZYK K,SCHMID C.Scale&affine invariant interest point detectors[J].International Journal of Computer Vision,2004,60(1):63-86.
[6] FREUND Y,DASGUPTA S,KABRA M,et al.Learning the structure of manifolds using random projections[C]∥NIPS’07Proceedings of the 20th International Conference on Neural Information Processing Systems.Red Hook,New York,USA:Curran Associates Inc.,2007:473-480.
[7] SCHINDLER G,BROWN M,SZELISKI R.City-scale location recognition[C]∥2007IEEE Conference on Computer Vision and Pattern Recognition.Washington,DC,USA:IEEE Computer Society,2007:1-7.
[8] XIE Bo,MU Yang,SONG Mingli,et al.Random projection tree and multiview embedding for large-scale image retrieval[C]∥ICONIP’10Proceedings of the17th International Conference on Neural Information Processing:Models and Applications.Berlin:SpringerVerlag,2010:641-649.
[9] DASGUPTA S,FREUND Y.Random projection trees for vector quantization[J].IEEE Transactions on Information Theory,2008,55(7):3229-3242.
[10]LOWE D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[11]LOWE D G.Object recognition from local scale-invariant features[C]∥ICCV’99Proceedings of the International Conference on Computer Vision.Washington,DC,USA:IEEE Computer Society,1999:1150-1157.
[12]NISTER D,STEWENIUS H.Scalable recognition with a vocabulary tree[C]∥2006IEEE Computer Society Conference on Computer Vision and Pattern Recognition.New York,USA:IEEE Computer Society,2006:2161-2168.
[13]LEPETIT V,LAGGER P,FUA P.Randomized trees for real-time keypoint recognition[C]∥CVPR’05Proceedings of the 2005IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington,DC,USA:IEEE Computer Society,2005:775-781.
[14]OBDRZALEK S,MATAS J.Sub-linear indexing for large scale object recognition[C]∥Proceedings of the British Machine Vision Conference.Durham,UK:BMVC,2005:1-10.
[15]JI Rongrong,DUAN Lingyu,CHEN Jie,et al.Learning to distribute vocabulary indexing for scalable visual search[J].IEEE Transactions on Multimedia,2013,15(1):153-166.
[16]JGOU H,DOUZE M,SCHMID C.Improving bag-offeatures for large scale image search[J].International Journal of Computer Vision,2010,87(3):316-336.
[17]WANG Xiaoyu,YANG Ming,COUR Timothee,et al.Contextual weighting for vocabulary tree based image retrieval[C]∥Computer Vision,IEEE International Conference.Washington,DC,USA:IEEE Computer Society,2011:209-216.
[18]朱道广,郭志刚,赵永威,等.基于空间上下文加权词汇树的图像检索方法[J].模式识别与人工智能,2013,26(11):1050-1056.(ZHU Daoguang,GUO Zhigang,ZHAO Yongwei,et al.Image retrieval with spatial context weighting based vocabulary tree[J].Pattern Recognition and Artificial Intelligence,2013,26(11):1050-1056.)
[19]何海清.低空摄影测量航带重构及数据处理方法研究[J].测绘学报,2014,43(4):440.(HE Haiqing.The study of strips reconstruction oriented data processing methodology in low-altitude photogrammetry[J].Acta Geodaetica et Cartographica Sinica,2014,43(4):440.)
[20]SIVIC J,ZISSERMAN A.Video Google:a text retrieval approach to object matching in videos[C]∥Proceeding of 9th IEEE International Conference on Computer Vision.Washington,DC,USA:IEEE Computer Society,2003:1470-1477.
[21]张剑清,潘励,王树根.摄影测量学[M].2版.武汉:武汉大学出版社,2009.(ZHANG Jianqing,PAN Li,WANG Shugen.Photogrammetry[M].2nd ed.Wuhan:Wuhan University Press,2009.)